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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Coelho, Leonardo Bertolucci
Université Libre de Bruxelles
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (5/5 displayed)
- 2024A microscopic view on the electrochemical deposition and dissolution of Au with scanning electrochemical cell microscopy – Part IIcitations
- 2023Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysiscitations
- 2023Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis
- 2023Self-healing plasma electrolytic oxidation (PEO) coating developed by an assembly of corrosion inhibitive layer and sol-gel sealing on AA2024citations
- 2021The effect of the substrate surface state on the morphology, topography and tribocorrosion behavior of Si/Zr sol-gel coated 316L stainless steelcitations
Places of action
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document
Estimating pitting descriptors of 316L stainless steel by machine learning and statistical analysis
Abstract
<jats:title>Abstract</jats:title><jats:p>A hybrid rule-base/ML approach using linear regression and artificial neural networks (ANN) determined pitting corrosion descriptors from high-throughput data obtained with Scanning Electrochemical Cell Microscopy (SECCM) on 316L stainless steel. Non-parametric density estimation determined the central tendencies of the E<jats:italic>pit</jats:italic>/log(<jats:italic>jpit</jats:italic>) and E<jats:italic>pass</jats:italic>/log(<jats:italic>jpass</jats:italic>) distributions. Descriptors estimated using conditional mean or median curves were compared to their central tendency values, with the conditional medians providing more accurate results. Due to their lower sensitivity to high outliers, the conditional medians were more robust representations of the log(<jats:italic>j</jats:italic>) Vs <jats:italic>E</jats:italic> distributions. An observed trend of passive range shortening with increasing testing aggressiveness was attributed to delayed stabilisation of the passive film, rather than early passivity breakdown.</jats:p>